Useful python for visualising neutron data in jupyter

SCIPP

Data framework for neutron TOF data

In [73]:
import scipp as sc
import mantid.simpleapi as manty
import plotly.graph_objs as go
import numpy as np
In [74]:
from scipp import Dim
In [75]:
d = sc.Dataset(
   ....:         {'a': sc.Variable(dims=[Dim.X, Dim.Y], values=np.random.rand(1000, 1000)),
   ....:          'b': sc.Variable(1.0)},
   ....:          coords={
   ....:              Dim.X: sc.Variable([Dim.X], values=np.arange(1000.0), unit=sc.units.m),
   ....:              Dim.Y: sc.Variable([Dim.Y], values=np.arange(1000.0), unit=sc.units.us)},
   ....:          labels={
   ....:              'aux': sc.Variable([Dim.Y], values=np.random.rand(1000))})
In [53]:
print(d)
sc.plot(d)

<scipp.Dataset>
Dimensions: {{Dim.Y, 1000}, {Dim.X, 1000}}
Coordinates:
    Dim.X                     double    [m]              (Dim.X)
    Dim.Y                     double    [μs]            (Dim.Y)
Labels:
    aux                       double    [dimensionless]  (Dim.Y)
Data:
    a                         double    [dimensionless]  (Dim.X, Dim.Y)
    b                         double    [dimensionless]  ()
Attributes:

Load a ISIS datafile as a mantid workspace object.

  • Use dir as way to list all the available method that object 'out' has
In [54]:
data = manty.Load('/home/jon/Sandbox/Data/MAR19717.raw')
In [143]:
def workspace2dToDataSet(workspace):
    signal = mtd[workspace].extractY()
    error  = mtd[workspace].extractE()
    TOF    = mtd[workspace].extractX()[0]
    wkspDim=signal.shape

    TOFAxis=(TOF[0:wkspDim[1]])
    d = sc.Dataset(
        {'data': sc.Variable(dims=[Dim.X, Dim.Y], values=signal),
        },
        coords={
        Dim.X: sc.Variable([Dim.X], values=np.arange(signal.shape[0]), unit=sc.units.m),
        Dim.Y: sc.Variable([Dim.Y], values=TOFAxis, unit=sc.units.us)},
        )
    print(d)
    return d
In [ ]:
In [145]:
d = workspace2dToDataSet('data')

<scipp.Dataset>
Dimensions: {{Dim.Y, 1900}, {Dim.X, 922}}
Coordinates:
    Dim.X                     int64     [m]              (Dim.X)
    Dim.Y                     double    [μs]            (Dim.Y)
Labels:
Data:
    data                      double    [dimensionless]  (Dim.X, Dim.Y)
Attributes:
In [146]:
sc.plot(d)

Making a simple UI to views the help documentation for the 2000 methods in the Mantid api

  1. Import ipywidgets
  2. Import mantid simple api
In [6]:
import ipywidgets as ipw
mantidList=(dir(manty))
from mantid.simpleapi import *

algorithum_list = ipw.Dropdown(
    options = mantidList,
    description='Mantid Simple API algorithums',
    disabled=False,
)


helpButton=ipw.Button(value=False,
    description='MantidHelp',
    disabled=False,
    button_style='info', # 'success', 'info', 'warning', 'danger' or ''
    tooltip='Description',
    icon='?')

display(algorithum_list)
display(helpButton)

out = ipw.Output(layout={'border': '1px solid black'})
display(out)

@out.capture()
def on_button_clicked(b):
    help(globals()[algorithum_list.get_interact_value()])

    
helpButton.on_click(on_button_clicked)

Import some helper scripts that can be used to reduce (treat) TOF neutron data from the MARI instrument

  • qtigenie is a sript that has the iliad function that will convert neutron TOF data to energy transfer data

  • This converts the units of Time of Flight in micro seconds to Energy Transfer in meV

In [7]:
import sys

sys.path.append('/home/jon/Sandbox/Scripts/qtiGenie/')
sys.path.append('/home/jon/Sandbox/Scripts/MARI/')
sys.path.append('/home/jon/Sandbox/Scripts/MARI/MapFiles/')

pathToData='/home/jon/Sandbox/Data/'

from qtiGenie import iliad
import ipywidgets as widgets

Using qtiplot graphics
You can change it by issuing setinst(InstrumentName) command
where InstrumentName can be MER, MAR, MAP, LET, TSK or XSD
In [8]:
from Direct.DirectEnergyConversion import *
import Direct.dgreduce as dgreduce

iliad_setup=dgreduce.setup
iliad=dgreduce.arb_units
iliad_abs=dgreduce.abs_units
    
iliad_reducer = dgreduce.getReducer

#@out.capture()
def reduceSimple(runNumber):
    inst='mar'
    iliad_setup(inst)
    ext='.raw'
    #mapfile='mari_res2013'
    mapfile='/home/jon/Sandbox/Scripts/MARI/MapFiles/mari_121_cor'

    #det_cal_file must be specified if the reduction sends out put to a workpsace
    cal_file=pathToData+'MAR19717.raw'
    #hard mask file
    #mask_file='/home/mari/Users/taylor/mask.msk'
    #load vanadium file
    whitebeamfile=pathToData+'MAR19717.raw'
    LoadRaw(Filename=whitebeamfile,OutputWorkspace="wb_wksp",LoadLogFiles="0")

    runs=pathToData+'MAR'+str(runNumber)+'.raw'; ei=12; rebin_params='-10,.05,11'; sum=False
    LoadRaw(Filename=runs,OutputWorkspace="run_wksp",LoadLogFiles="0")
    w1=iliad("wb_wksp","run_wksp",ei,rebin_params,mapfile,fixei=False,det_cal_file=cal_file,norm_method='current')

We could call reduce simple in a cell Instead we can make a small UI to run it for us using a widget and sending the output to a text widget with @capture

In [9]:
button1 = widgets.Button(description="Reduce Data")
display(button1)

button2 = widgets.Button(description="Clear")
display(button2)

text_field=widgets.Text(value='run number')
display(text_field)

TextOutput = widgets.Output(layout={'border': '1px solid black'})
display(TextOutput)

@TextOutput.capture()
def on_button_clicked(b):
    runNumber = text_field.value
    print ('Running Iliad on file ',runNumber)
    reduceSimple(runNumber)
    ei = mtd['w1'].getRun().getLogData("Ei").value
    print('Incident Neutron Energy for run', runNumber,' determined as ',ei,'meV')

@TextOutput.capture()
def on_clear_clicked(b):
    TextOutput.clear_output()
  
    

button1.on_click(on_button_clicked)
button2.on_click(on_clear_clicked)

The mantid workspace has all the instrument & run meta data

  • We will get the calculated incident energy from the workspace object
In [11]:
incident_energy=mtd['w1'].getRun().getLogData("Ei").value
print (incident_energy,'meV')

12.045315866660905 meV

We will make a UI to list and plot all the mantid workspaces that exisit in the kernal session

In [12]:
%matplotlib inline
import matplotlib.pyplot as plt


mtd.importAll()
workSpaceList = %who_ls Workspace2D
workSpaceListWidget = widgets.Dropdown(
    options=workSpaceList,
    description='WorkSpace:',
    disabled=False,
)

buttonSelectWorkspace = widgets.Button(description="Select Workspace",layout=widgets.Layout(width='auto', grid_area='main'))
#display(buttonPlotSpectrum)

buttonPlotSpectrum = widgets.Button(description="plot spectrum",layout=widgets.Layout(width='auto', grid_area='main'))
#display(buttonPlotSpectrum)

buttonPlot2D = widgets.Button(description="plot 2D",layout=widgets.Layout(width='auto', grid_area='main'))
#display(buttonPlot2D)

clearPlots = widgets.Button(description="ClearPlots",layout=widgets.Layout(width='auto', grid_area='main'))
#display(buttonPlot2D)

text_field_spec = widgets.Text(value='Spectrum_number')

PlotOutput = widgets.Output(layout={'border': '1px solid black'})

TextOutput = widgets.Output(layout={'border': '1px solid black'})


list_widgets  = [
    widgets.VBox([workSpaceListWidget,buttonSelectWorkspace,TextOutput]),
    widgets.VBox([buttonPlotSpectrum,text_field_spec]),
    buttonPlot2D, widgets.VBox([clearPlots, PlotOutput])]

accordion = widgets.Accordion(children=list_widgets)
accordion.set_title(0, 'Select Data')
accordion.set_title(1, '1D Plot')
accordion.set_title(2, '2D Plot')
accordion.set_title(3, 'Plot Output')
display(accordion)

@PlotOutput.capture()
def on_plotSpectrum_button_clicked(a):
    specNumber = int(text_field_spec.value)
    print (type (specNumber),specNumber)
    selectedWorkspace = workSpaceListWidget.value
    dataY = mtd[selectedWorkspace].extractY()
    plt.figure()
    plt.plot(dataY[specNumber,:])
    plt.show()

    
@PlotOutput.capture()
def on_plot2D_button_clicked(b):
    
    selectedWorkspace = workSpaceListWidget.value
    dataY = mtd[selectedWorkspace].extractY()
    plt.figure()
    plt.imshow(dataY,vmin=0,vmax=10,aspect='auto')
    plt.show() 
    
@PlotOutput.capture()
def on_clearPlots_button_clicked(c):
    plt.close('all')
    PlotOutput.clear_output()

@PlotOutput.capture()
def on_clear_clicked(d):
    out.clear_output()
  
@TextOutput.capture()
def on_buttonSelectWorkspace_button_clicked(e):
    selectedWorkspace = workSpaceListWidget.value
    dataY = mtd[selectedWorkspace].extractY()
    print (dataY.shape)
    return dataY,selectedWorkspace
 
   

buttonPlotSpectrum.on_click(on_plotSpectrum_button_clicked)
buttonPlot2D.on_click(on_plot2D_button_clicked)
clearPlots.on_click(on_clearPlots_button_clicked)

Visualising neutron TOF data

Holoviews is a very adaptable visualisation package

  • It requires a defined data structure
  • Pandas ( for Tabular data)
  • Xarray for gridded data

What is useful about these types of packages is that the data structure is well defined with diminesion names to make subsequent operations more straighforwards

There are limitations, xarray doesn't know about errors or anything that specific to neutron or photon data like scientific units or geometry.

  1. First load some data.
  2. Write a small function that converts the TOF and signal values from a mantid workspace to a holoviews dataset.
In [16]:
def mantidWkspToHoloViewDataSet(wkspname):
    import holoviews as hv
    signal = mtd[wkspname].extractY()
    error  = mtd[wkspname].extractE()
    TOF    = mtd[wkspname].extractX()
    wkspDim=signal.shape
    dataCopy=np.zeros([wkspDim[0],wkspDim[1],2])
    dataCopy[:,:,0]=signal
    dataCopy[:,:,1]=error
    ds = hv.Dataset((np.arange(wkspDim[1]),np.arange(wkspDim[0]),dataCopy[:,:,0]),['x', 'y'], 'Counts')
    #ingore errors
    return ds
In [24]:
import holoviews as hv
from holoviews import opts


hvDs = mantidWkspToHoloViewDataSet('w1')


points = hvDs.to(hv.Image)
points.opts(width=500, height=500)
Out[24]:

So when we plot the 2d data we dont know really anything about the axis. That has to be added by the user, or by using a different data structure

Also note that the color mapping of the z axis does not make it easy to visualise. At first glance it looks like a sparse data set.

  • This scaling issue of Z in 2D plots is common in visualistaion of scientific data. Most packages assume that the dynamic range of the data is low. When in this case (inelastic neutron scattering it is not)

  • We can uses the functionality of holoviews to make a data explorer widget.

Lets write a 'super plot' function.

In [20]:
import holoviews as hv
from holoviews import opts
hv.extension('bokeh')

opts.defaults(
    opts.GridSpace(shared_xaxis=True, shared_yaxis=True),
    opts.Image(cmap='jet', width=500, height=500),
    opts.Labels(text_color='white', text_font_size='8pt', text_align='left', text_baseline='bottom'),
    opts.Path(color='white'),
    opts.Spread(width=600),
    opts.Overlay(show_legend=False),
    )
def superplot1(wksp):
    hvDs = mantidWkspToHoloViewDataSet(wksp)
    img = hvDs.to(hv.Image).opts(colorbar=True, colorbar_position = 'left')

    posy = hv.streams.PointerY()
    hline = hv.DynamicMap(lambda y: hv.HLine(y or -100), streams=[posy])

    posx = hv.streams.PointerX()
    vline = hv.DynamicMap(lambda x: hv.VLine(x or -100), streams=[posx])


    crosssection2 = hv.DynamicMap(lambda y: img.sample(y=y if y else 0), streams=[posy])
    crosssection1 = hv.DynamicMap(lambda x: img.sample(x=x if x else 0), streams=[posx])

# Combine images, vline and cross-sections
#((img1 * hline * vline) << crosssection1)+((img1 * vline) << crosssection1)

    return ((img * hline * vline) << crosssection1 << crosssection2)
In [21]:
superplot1('w1')
Out[21]:

The lowest value of the color map does not capture the fine detail in the plot.

we could use a log fucntion, or we could play with the Z scale itself. This however actually sucks

  • Everytime a plot is made the z axis must be evaluated
  • In code thats rubbish.

There is another way - Shade the data

In [47]:
import numpy as np
import holoviews as hv
from holoviews import opts
from holoviews import streams
from matplotlib import cm
hv.extension('bokeh')

from holoviews.operation.datashader import datashade

opts.defaults(
    opts.GridSpace(shared_xaxis=True, shared_yaxis=True),
    opts.Image(cmap='jet', width=700, height=700),
    opts.Labels(text_color='white', text_font_size='8pt', text_align='left', text_baseline='bottom'),
    opts.Path(color='white'),
    opts.Spread(width=600),
    opts.Overlay(show_legend=False),
    )


ds = mantidWkspToHoloViewDataSet('w1')



#datashade(ds)
points = ds.to(hv.Image).opts(width=300, height=300)
shaded = datashade(points,cmap=cm.viridis).opts(width=300, height=300)
points + shaded
Out[47]:

The visual difference between the shaded and not shaded data is apparent

We will rewrite the super plot to use data shader with a linear colormap from Matplotlib
In [22]:
import numpy as np
import holoviews as hv
from holoviews import opts
from holoviews import streams

from holoviews.operation.datashader import datashade

from matplotlib import cm

hv.extension('bokeh')

opts.defaults(
    opts.GridSpace(shared_xaxis=True, shared_yaxis=True),
    opts.Image(cmap='jet', width=700, height=700),
    opts.Labels(text_color='white', text_font_size='8pt', text_align='left', text_baseline='bottom'),
    opts.Path(color='white'),
    opts.Spread(width=600),
    opts.Overlay(show_legend=False),
    )
opts.defaults(opts.Histogram(framewise=True))

#ds = hv.Dataset((np.arange(2624),np.arange(922),dataCopy[:,:,0]),['x', 'y'], 'Counts')
#ds.data

#def holoViewInteractive2D(hvDataSet):
    # Declare distribution of Points

def superplot2(wksp):    
    hvDs = mantidWkspToHoloViewDataSet(wksp)
    points = hvDs.to(hv.Image)
    points.opts(colorbar=True, colorbar_position = 'left')

    # Declare a Bounds stream and DynamicMap to get box_select geometry and draw it
    box = streams.BoundsXY(source=points, bounds=(0,0,0,0))
    bounds = hv.DynamicMap(lambda bounds: hv.Bounds(bounds), streams=[box])

    # Declare DynamicMap to apply bounds selection
    dmap = hv.DynamicMap(lambda bounds: points.select(x=(bounds[0], bounds[2]),y=(bounds[1], bounds[3])),streams=[box])

    # Compute histograms of selection along x-axis and y-axis
    yhist = hv.operation.histogram(dmap, bin_range=points.range('y'), dimension='y', num_bins = 500, weight_dimension='Counts', mean_weighted=True)
    xhist = hv.operation.histogram(dmap, bin_range=points.range('x'), dimension='x', num_bins = 500, weight_dimension='Counts', mean_weighted=True)


    #hv.Curve(selection.aggregate('Time', np.mean))

    #roi = ds.select(x=(bounds[0], bounds[2]),y=(bounds[1], bounds[3]), ).relabel('ROI')
    #region = roi.to(hv.Image, ['x', 'y'])

    # Combine components and display
    #points * mean_sel * bounds << yhist << xhist

    shaded = datashade(points,cmap=cm.viridis).opts(width=500, height=500)
    return (shaded * bounds) << yhist << xhist
    

#holoViewInteractive2D(hvDs)
In [23]:
superplot2('w1')

The keyword argument 'parallel=True' was specified but no transformation for parallel execution was possible.

To find out why, try turning on parallel diagnostics, see http://numba.pydata.org/numba-doc/latest/user/parallel.html#diagnostics for help.

File "../../../lib/python3.6/site-packages/datashader/resampling.py", line 235:
@ngjit_parallel
def _get_dimensions(src, out):
^

invalid value encountered in true_divide
Out[23]:
Now that looks better without having to think of the z axis scale.
Adding histograms rather than line plots allows better evaluation of the structre of the data
  • Now we can convert the data from a grid to Momemtum Transfer (|Q|)
In [45]:
Qdata=SofQWNormalisedPolygon(InputWorkspace=w1,QAxisBinning='0,0.01,5',Emode='Direct',EFixed=12,ReplaceNaNs = True)
In [46]:
superplot2('Qdata')

invalid value encountered in true_divide
Out[46]:
  • Holowviews can be easily used to buld up complex publication quality plots
  • Having a note book environment allows all the data reduction and visualisation processing to be saved and documented
  • in a variety of formats.

There are many python packages for data analysis and modelling.

What is important is to be able to save the meta data for a model in a defined format

  • Interoperability between softwares is benificial

This is the motivation for the Atomic Simulation Environment (ASE)

  • A python Package that acts as a abstraction layer between simulation codes and python

  • Lets use that to run a quick simulation of the Phonon modes in Aluminium.

  • For inelastic sccattering where ther signal is always small its a good idea to know what contributions can come from common materials found in the experiment like Aluminium

In [25]:
from ase.build import bulk
from ase.calculators.emt import EMT
from ase.phonons import Phonons

# Setup crystal and EMT calculator
atoms = bulk('Al', 'fcc', a=4.05)

# Phonon calculator
N = 7
ph = Phonons(atoms, EMT(), supercell=(N, N, N), delta=0.05)
ph.run()

# Read forces and assemble the dynamical matrix
ph.read(acoustic=True)
ph.clean()

path = atoms.cell.bandpath('GXULGK', npoints=100)
bs = ph.get_band_structure(path)

dos = ph.get_dos(kpts=(20, 20, 20)).sample_grid(npts=100, width=1e-3)

Writing phonon.eq.pckl
Writing phonon.0x-.pckl
Writing phonon.0x+.pckl
Writing phonon.0y-.pckl
Writing phonon.0y+.pckl
Writing phonon.0z-.pckl
Writing phonon.0z+.pckl
In [26]:
# Plot the band structure and DOS:
%matplotlib inline
import matplotlib.pyplot as plt


fig = plt.figure(1, figsize=(7, 4))
ax = fig.add_axes([.12, .07, .67, .85])

emax = 0.035
bs.plot(ax=ax, show=False, emin=0.0, emax=emax)
aa=bs.todict()
aa.keys
dosax = fig.add_axes([.8, .07, .17, .85])
dosax.fill_between(dos.weights[0], dos.energy, y2=0, color='grey',edgecolor='k', lw=1)

dosax.set_ylim(0, emax)
dosax.set_yticks([])
dosax.set_xticks([])
dosax.set_xlabel("DOS", fontsize=18)
Out[26]:
Text(0.5, 0, 'DOS')
In [27]:
from ase.visualize import view
view(atoms,viewer='ngl')
In [ ]:

Data Management from within jupyter

We can connect our jupyter session to a data catalouge and then search the catalouge.

In [29]:
import requests
import json
import urllib

def search_scicat(text, max_number_results):
    fields = {'text': text}
    limit = {'limit': max_number_results, 'order': "creationTime:desc"}
    fields_encode = urllib.parse.quote(json.dumps(fields))
    limit_encode = urllib.parse.quote(json.dumps(limit))
    dataset_url = "https://scicatapi.esss.dk/api/v3/Datasets/anonymousquery?fields=" + \
                fields_encode+"&limits="+limit_encode
    r=requests.get(dataset_url).json()
    print(len(r), "result found!")
    return r
In [33]:
results=search_scicat("Al",100)

63 result found!

There are 63 results found with the keyword 'Al' the results data object contains all of the meta data locations for those results.

In [36]:
results
Out[36]:
[{'owner': 'Clement Derrez',
  'ownerEmail': 'Clement.Derrez@esss.se',
  'contactEmail': 'Clement.Derrez@esss.se',
  'sourceFolder': '/users/detector/experiments/beamInstrumentation/FMC-PICO-evaluation',
  'size': 1792968,
  'packedSize': 1792968,
  'creationTime': '2019-03-29T10:02:27.000Z',
  'type': 'raw',
  'validationStatus': 'valid',
  'keywords': ['BeamInstrumentation', 'neutron', 'detector'],
  'description': 'This data was collected as part of the beam instrumentation program https://github.com/ess-dmsc/ess_file_formats/wiki/HDF5',
  'datasetName': 'Sample Data from BeamInstrumentation 3',
  'license': 'ESS',
  'version': '3.01',
  'isPublished': True,
  'ownerGroup': 'ess',
  'accessGroups': ['brightness', 'ess'],
  'updatedBy': 'ingestor',
  'updatedAt': '2019-04-30T11:51:21.959Z',
  'datasetlifecycle': {'archivable': True,
   'retrievable': False,
   'dateOfDiskPurging': '2029-03-29T09:02:27.966Z',
   'archiveRetentionTime': '2029-03-29T09:02:27.966Z',
   'dateOfPublishing': '2022-03-29T09:02:27.966Z',
   'archiveStatusMessage': 'Stored on primary disk and on tape',
   'retrieveStatusMessage': 'string',
   'archiveReturnMessage': {'string': 'string'},
   'retrieveReturnMessage': {'string': 'string'},
   'exportedTo': 'string'},
  'principalInvestigator': 'Clement Derrez',
  'creationLocation': 'BeamInstrumentation',
  'scientificMetadata': {'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0/Channel_range': '0',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0/FMC SN': '18001',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0/Load': '100m AMGAB + IC',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0_OC/Channel_range': '0',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0_OC/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0_OC/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0_OC/FMC SN': '18001',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0_OC/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0_OC/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0_OC/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0_OC/Load': 'OC',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0_OC/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG0_OC/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1/Channel_range': '1',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1/FMC SN': '18001',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1/Load': '100m AMGAB + IC',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1_OC/Channel_range': '1',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1_OC/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1_OC/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1_OC/FMC SN': '18001',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1_OC/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1_OC/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1_OC/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1_OC/Load': 'OC',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1_OC/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH1_RNG1_OC/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0/Channel_range': '0',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0/FMC SN': '18001',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0/Load': '100m AMGAB + IC',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0_OC/Channel_range': '0',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0_OC/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0_OC/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0_OC/FMC SN': '18001',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0_OC/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0_OC/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0_OC/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0_OC/Load': 'OC',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0_OC/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG0_OC/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1/Channel_range': '1',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1/FMC SN': '18001',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1/Load': '100m AMGAB + IC',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1_OC/Channel_range': '1',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1_OC/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1_OC/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1_OC/FMC SN': '18001',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1_OC/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1_OC/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1_OC/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1_OC/Load': 'OC',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1_OC/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH2_RNG1_OC/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0/Channel_range': '0',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0/FMC SN': '18001',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0/Load': '100m AMGAB + IC',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0_OC/Channel_range': '0',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0_OC/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0_OC/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0_OC/FMC SN': '18001',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0_OC/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0_OC/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0_OC/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0_OC/Load': 'OC',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0_OC/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG0_OC/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1/Channel_range': '1',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1/FMC SN': '18001',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1/Load': '100m AMGAB + IC',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1_OC/Channel_range': '1',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1_OC/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1_OC/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1_OC/FMC SN': '18001',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1_OC/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1_OC/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1_OC/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1_OC/Load': 'OC',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1_OC/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH3_RNG1_OC/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0/Channel_range': '0',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0/FMC SN': '18001',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0/Load': '100m AMGAB + IC',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0_OC/Channel_range': '0',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0_OC/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0_OC/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0_OC/FMC SN': '18001',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0_OC/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0_OC/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0_OC/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0_OC/Load': 'OC',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0_OC/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG0_OC/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1/Channel_range': '1',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1/FMC SN': '18001',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1/Load': '100m AMGAB + IC',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1_OC/Channel_range': '1',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1_OC/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1_OC/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1_OC/FMC SN': '18001',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1_OC/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1_OC/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1_OC/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1_OC/Load': 'OC',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1_OC/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18001_CH4_RNG1_OC/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0/Channel_range': '0',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0/ion chamber SN': 'HCBLM I001-05005107',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0/FMC SN': '18002',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0/Load': '100m AMGAB + IC',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0_OC/Channel_range': '0',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0_OC/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0_OC/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0_OC/FMC SN': '18002',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0_OC/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0_OC/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0_OC/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0_OC/Load': 'OC',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0_OC/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG0_OC/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1/Channel_range': '1',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1/ion chamber SN': 'HCBLM I001-05005107',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1/FMC SN': '18002',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1/Load': '100m AMGAB + IC',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1_OC/Channel_range': '1',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1_OC/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1_OC/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1_OC/FMC SN': '18002',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1_OC/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1_OC/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1_OC/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1_OC/Load': 'OC',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1_OC/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH1_RNG1_OC/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0/Channel_range': '0',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0/ion chamber SN': 'HCBLM I001-05005107',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0/FMC SN': '18002',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0/Load': '100m AMGAB + IC',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0_OC/Channel_range': '0',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0_OC/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0_OC/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0_OC/FMC SN': '18002',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0_OC/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0_OC/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0_OC/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0_OC/Load': 'OC',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0_OC/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG0_OC/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG1/Channel_range': '1',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG1/ion chamber SN': 'HCBLM I001-05005107',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG1/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG1/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG1/FMC SN': '18002',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG1/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG1/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG1/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG1/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG1/Load': '100m AMGAB + IC',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG1/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG1/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG1_OC/Channel_range': '1',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG1_OC/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG1_OC/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG1_OC/FMC SN': '18002',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG1_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG1_OC/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG1_OC/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG1_OC/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG1_OC/Load': 'OC',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG1_OC/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH2_RNG1_OC/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG0/Channel_range': '0',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG0/ion chamber SN': 'HCBLM I001-05005107',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG0/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG0/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG0/FMC SN': '18002',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG0/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG0/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG0/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG0/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG0/Load': '100m AMGAB + IC',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG0/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG0/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG0_OC/Channel_range': '0',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG0_OC/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG0_OC/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG0_OC/FMC SN': '18002',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG0_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG0_OC/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG0_OC/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG0_OC/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG0_OC/Load': 'OC',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG0_OC/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG0_OC/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG1/Channel_range': '1',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG1/ion chamber SN': 'HCBLM I001-05005107',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG1/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG1/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG1/FMC SN': '18002',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG1/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG1/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG1/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG1/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG1/Load': '100m AMGAB + IC',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG1/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG1/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG1_OC/Channel_range': '1',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG1_OC/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG1_OC/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG1_OC/FMC SN': '18002',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG1_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG1_OC/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG1_OC/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG1_OC/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG1_OC/Load': 'OC',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG1_OC/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH3_RNG1_OC/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG0/Channel_range': '0',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG0/ion chamber SN': 'HCBLM I001-05005107',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG0/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG0/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG0/FMC SN': '18002',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG0/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG0/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG0/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG0/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG0/Load': '100m AMGAB + IC',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG0/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG0/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG0_OC/Channel_range': '0',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG0_OC/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG0_OC/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG0_OC/FMC SN': '18002',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG0_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG0_OC/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG0_OC/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG0_OC/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG0_OC/Load': 'OC',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG0_OC/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG0_OC/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG1/Channel_range': '1',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG1/ion chamber SN': 'HCBLM I001-05005107',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG1/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG1/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG1/FMC SN': '18002',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG1/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG1/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG1/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG1/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG1/Load': '100m AMGAB + IC',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG1/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG1/Data type': 'FLoat32',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG1_OC/Channel_range': '1',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG1_OC/Sampling rate': '1000000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG1_OC/Nb of points': '10000',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG1_OC/FMC SN': '18002',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG1_OC/FMC VERSION': 'FMC-Pico-1m4-C3',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG1_OC/Operator': 'Clement Derrez',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG1_OC/Location': 'BD lab room 4',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG1_OC/mTCA crate ID': '675',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG1_OC/Load': 'OC',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG1_OC/Unit': 'A',
   'FMC-PICO/FMC-Pico-1m4-C3/18002_CH4_RNG1_OC/Data type': 'FLoat32',
   'datetime': '2019-04-30T11:49:41.552Z'},
  'endTime': '2019-03-29T10:02:27.966Z',
  'proposalId': 'MRV1E2',
  'sampleId': 'samplebeaminstrumentation0003',
  'pid': '20.500.12269/BRIGHTNESS/BeamInstrumentation0003'},
 {'owner': 'Anton Khaplanov',
  'ownerEmail': 'anton.khaplanov@esss.se',
  'orcidOfOwner': 'https://orcid.org/0000-0002-8421-1184',
  'contactEmail': 'anton.khaplanov@esss.se',
  'sourceFolder': '/users/detector/experiments/multigrid/data/raw/SNS_Aug18/efu_dump/9-3 14-36 Si_Crystal',
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  'creationTime': '2018-09-04T16:39:43.000Z',
  'type': 'raw',
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  'keywords': ['multigrid', 'neutron', 'detector'],
  'description': 'This data was collected as part of BrightnESS, funded by the European Union                         Framework Programme for Research and Innovation Horizon 2020, under grant                         agreement 676548. It consists of test data for the detector. https://github.com/ess-dmsc/ess_file_formats/wiki/Multigrid-Data-Format-I',
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   'archiveRetentionTime': '2028-09-04T16:39:43.000Z',
   'dateOfPublishing': '2021-09-04T16:39:43.000Z',
   'archiveStatusMessage': 'Stored on primary disk and on tape',
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  'principalInvestigator': 'Anton Khaplanov',
  'creationLocation': 'multigrid',
  'scientificMetadata': {'Time': '2018-09-02 16:40:31.596',
   'BL17:CS:IPTS': '21308.0',
   'BL17:CS:RunControl:LastRunNumber': '154380.0',
   'BL17:Chop:Skf1:EnergyReq': {'u': 'MeV', 'v': '250.0'},
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   'BL17:Chop:Skf3:EnergyReq': {'u': 'MeV', 'v': '250.0'},
   'BL17:Chop:Skf3:SpeedReq': {'u': 'Hz', 'v': '120.0'},
   'BL17:Det:PCharge:C': {'u': 'C', 'v': '1.0000709428200283'},
   'BL17:Mot:Sample:Axis1': '0.0',
   'BL17:Mot:attenuator1': {'u': 'mm', 'v': '0.1'},
   'BL17:Mot:attenuator2': {'u': 'mm', 'v': '93.4'},
   'BL17:Mot:chtrans': {'u': 'mm', 'v': '-182.0'},
   'BL17:SMS:RunInfo:RunTitle': '5x5 van',
   'field16': 'White beam',
   'field17': 'Ei set to 250meV T0=30',
   'datetime': '2018-09-03T14:36:00.000Z'},
  'endTime': '2018-09-04T16:39:43.000Z',
  'proposalId': '439YZU',
  'sampleId': 'samplemultigrid0032',
  'pid': '20.500.12269/BRIGHTNESS/MG0032'},
 {'owner': 'Ramsey Al Jebali',
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  'orcidOfOwner': 'https://orcid.org/0000-0000-0000-0000',
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  'sourceFolder': '/users/detector/experiments/sonde/IFE_june_2018/data/S17',
  'size': 1153220552,
  'packedSize': 1153220552,
  'creationTime': '2018-06-08T12:10:23.000Z',
  'type': 'raw',
  'validationStatus': 'valid',
  'keywords': ['SoNDe', 'neutron', 'detector'],
  'description': 'This data was collected as part of BrightnESS, funded by the European Union                         Framework Programme for Research and Innovation Horizon 2020, under grant                         agreement 676548. It consists of test data for the detector. https://github.com/ess-dmsc/ess_file_formats/wiki/SONDE',
  'datasetName': 'ACRO mode, /w mask, thrs: 10',
  'license': 'ESS',
  'version': '3.01',
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  'updatedBy': 'ingestor',
  'updatedAt': '2019-04-30T11:50:16.584Z',
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  'datasetName': 'Central measurement of P19 @ 2.0Å',
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   'subject': 'Central measurement of P19 @ 2.0Å',
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  'datasetName': 'Sample Data from NMX 101',
  'license': 'ESS',
  'version': '3.01',
  'isPublished': True,
  'ownerGroup': 'ess',
  'accessGroups': ['brightness', 'ess'],
  'updatedBy': 'ingestor',
  'updatedAt': '2019-04-30T11:50:08.435Z',
  'datasetlifecycle': {'archivable': True,
   'retrievable': False,
   'dateOfDiskPurging': '2026-12-09T16:20:01.000Z',
   'archiveRetentionTime': '2026-12-09T16:20:01.000Z',
   'dateOfPublishing': '2019-12-09T16:20:01.000Z',
   'archiveStatusMessage': 'Stored on primary disk and on tape',
   'retrieveStatusMessage': 'string',
   'archiveReturnMessage': {'string': 'string'},
   'retrieveReturnMessage': {'string': 'string'},
   'exportedTo': 'string'},
  'principalInvestigator': 'Dorothea Pfeiffer',
  'creationLocation': 'NMX',
  'scientificMetadata': {'id': 3, 'datetime': '2019-04-30T11:49:36.672Z'},
  'endTime': '2016-12-09T16:20:01.000Z',
  'proposalId': '2LG2QT',
  'sampleId': 'samplenmx0101',
  'pid': '20.500.12269/BRIGHTNESS/NMX0101'},
 {'owner': 'Dorothea Pfeiffer',
  'ownerEmail': 'Dorothea.Pfeiffer@esss.se',
  'orcidOfOwner': 'https://orcid.org/0000-0003-3893-2308',
  'contactEmail': 'Dorothea.Pfeiffer@esss.se',
  'sourceFolder': '/users/detector/experiments/nmx/data/h5/raw/IFE_2016_Nov/Calibration/run_136_Sisi_G_3300V_D_3926V_back_2p00A_unf_APZ_280_cal.h5',
  'size': 1001230152,
  'packedSize': 1001230152,
  'creationTime': '2016-12-09T16:12:27.000Z',
  'type': 'raw',
  'validationStatus': 'valid',
  'keywords': ['NMX', 'neutron', 'detector'],
  'description': 'This data was collected as part of BrightnESS, funded by the European Union                         Framework Programme for Research and Innovation Horizon 2020, under grant                         agreement 676548. It consists of test data for the detector. https://github.com/ess-dmsc/ess_file_formats/wiki/NMX',
  'datasetName': 'Sample Data from NMX 100',
  'license': 'ESS',
  'version': '3.01',
  'isPublished': True,
  'ownerGroup': 'ess',
  'accessGroups': ['brightness', 'ess'],
  'updatedBy': 'ingestor',
  'updatedAt': '2019-04-30T11:50:08.226Z',
  'datasetlifecycle': {'archivable': True,
   'retrievable': False,
   'dateOfDiskPurging': '2026-12-09T16:12:27.000Z',
   'archiveRetentionTime': '2026-12-09T16:12:27.000Z',
   'dateOfPublishing': '2019-12-09T16:12:27.000Z',
   'archiveStatusMessage': 'Stored on primary disk and on tape',
   'retrieveStatusMessage': 'string',
   'archiveReturnMessage': {'string': 'string'},
   'retrieveReturnMessage': {'string': 'string'},
   'exportedTo': 'string'},
  'principalInvestigator': 'Dorothea Pfeiffer',
  'creationLocation': 'NMX',
  'scientificMetadata': {'id': 3, 'datetime': '2019-04-30T11:49:36.672Z'},
  'endTime': '2016-12-09T16:12:27.000Z',
  'proposalId': '2LG2QT',
  'sampleId': 'samplenmx0100',
  'pid': '20.500.12269/BRIGHTNESS/NMX0100'},
 {'owner': 'Dorothea Pfeiffer',
  'ownerEmail': 'Dorothea.Pfeiffer@esss.se',
  'orcidOfOwner': 'https://orcid.org/0000-0003-3893-2308',
  'contactEmail': 'Dorothea.Pfeiffer@esss.se',
  'sourceFolder': '/users/detector/experiments/nmx/data/h5/raw/IFE_2016_Nov/Calibration/run_135_Sisi_G_3330V_D_3348V_back_2p00A_unf_APZ_280_cal.h5',
  'size': 966133790,
  'packedSize': 966133790,
  'creationTime': '2016-12-09T16:04:56.000Z',
  'type': 'raw',
  'validationStatus': 'valid',
  'keywords': ['NMX', 'neutron', 'detector'],
  'description': 'This data was collected as part of BrightnESS, funded by the European Union                         Framework Programme for Research and Innovation Horizon 2020, under grant                         agreement 676548. It consists of test data for the detector. https://github.com/ess-dmsc/ess_file_formats/wiki/NMX',
  'datasetName': 'Sample Data from NMX 77',
  'license': 'ESS',
  'version': '3.01',
  'isPublished': True,
  'ownerGroup': 'ess',
  'accessGroups': ['brightness', 'ess'],
  'updatedBy': 'ingestor',
  'updatedAt': '2019-04-30T11:50:08.034Z',
  'datasetlifecycle': {'archivable': True,
   'retrievable': False,
   'dateOfDiskPurging': '2026-12-09T16:04:56.000Z',
   'archiveRetentionTime': '2026-12-09T16:04:56.000Z',
   'dateOfPublishing': '2019-12-09T16:04:56.000Z',
   'archiveStatusMessage': 'Stored on primary disk and on tape',
   'retrieveStatusMessage': 'string',
   'archiveReturnMessage': {'string': 'string'},
   'retrieveReturnMessage': {'string': 'string'},
   'exportedTo': 'string'},
  'principalInvestigator': 'Dorothea Pfeiffer',
  'creationLocation': 'NMX',
  'scientificMetadata': {'id': 3, 'datetime': '2019-04-30T11:49:36.672Z'},
  'endTime': '2016-12-09T16:04:56.000Z',
  'proposalId': '2LG2QT',
  'sampleId': 'samplenmx0077',
  'pid': '20.500.12269/BRIGHTNESS/NMX0077'},
 {'owner': 'Dorothea Pfeiffer',
  'ownerEmail': 'Dorothea.Pfeiffer@esss.se',
  'orcidOfOwner': 'https://orcid.org/0000-0003-3893-2308',
  'contactEmail': 'Dorothea.Pfeiffer@esss.se',
  'sourceFolder': '/users/detector/experiments/nmx/data/h5/raw/IFE_2016_Nov/Calibration/run_134_Sisi_G_3330V_D_3648V_back_2p00A_unf_APZ_280_cal.h5',
  'size': 999595160,
  'packedSize': 999595160,
  'creationTime': '2016-12-09T15:57:30.000Z',
  'type': 'raw',
  'validationStatus': 'valid',
  'keywords': ['NMX', 'neutron', 'detector'],
  'description': 'This data was collected as part of BrightnESS, funded by the European Union                         Framework Programme for Research and Innovation Horizon 2020, under grant                         agreement 676548. It consists of test data for the detector. https://github.com/ess-dmsc/ess_file_formats/wiki/NMX',
  'datasetName': 'Sample Data from NMX 76',
  'license': 'ESS',
  'version': '3.01',
  'isPublished': True,
  'ownerGroup': 'ess',
  'accessGroups': ['brightness', 'ess'],
  'updatedBy': 'ingestor',
  'updatedAt': '2019-04-30T11:50:07.812Z',
  'datasetlifecycle': {'archivable': True,
   'retrievable': False,
   'dateOfDiskPurging': '2026-12-09T15:57:30.000Z',
   'archiveRetentionTime': '2026-12-09T15:57:30.000Z',
   'dateOfPublishing': '2019-12-09T15:57:30.000Z',
   'archiveStatusMessage': 'Stored on primary disk and on tape',
   'retrieveStatusMessage': 'string',
   'archiveReturnMessage': {'string': 'string'},
   'retrieveReturnMessage': {'string': 'string'},
   'exportedTo': 'string'},
  'principalInvestigator': 'Dorothea Pfeiffer',
  'creationLocation': 'NMX',
  'scientificMetadata': {'id': 3, 'datetime': '2019-04-30T11:49:36.672Z'},
  'endTime': '2016-12-09T15:57:30.000Z',
  'proposalId': '2LG2QT',
  'sampleId': 'samplenmx0076',
  'pid': '20.500.12269/BRIGHTNESS/NMX0076'},
 {'owner': 'Dorothea Pfeiffer',
  'ownerEmail': 'Dorothea.Pfeiffer@esss.se',
  'orcidOfOwner': 'https://orcid.org/0000-0003-3893-2308',
  'contactEmail': 'Dorothea.Pfeiffer@esss.se',
  'sourceFolder': '/users/detector/experiments/nmx/data/h5/raw/IFE_2016_Nov/Calibration/run_132_Sisi_G_3330V_D_3948V_back_2p00A_unf_APZ_280_cal.h5',
  'size': 997198063,
  'packedSize': 997198063,
  'creationTime': '2016-12-09T15:50:01.000Z',
  'type': 'raw',
  'validationStatus': 'valid',
  'keywords': ['NMX', 'neutron', 'detector'],
  'description': 'This data was collected as part of BrightnESS, funded by the European Union                         Framework Programme for Research and Innovation Horizon 2020, under grant                         agreement 676548. It consists of test data for the detector. https://github.com/ess-dmsc/ess_file_formats/wiki/NMX',
  'datasetName': 'Sample Data from NMX 75',
  'license': 'ESS',
  'version': '3.01',
  'isPublished': True,
  'ownerGroup': 'ess',
  'accessGroups': ['brightness', 'ess'],
  'updatedBy': 'ingestor',
  'updatedAt': '2019-04-30T11:50:07.634Z',
  'datasetlifecycle': {'archivable': True,
   'retrievable': False,
   'dateOfDiskPurging': '2026-12-09T15:50:01.000Z',
   'archiveRetentionTime': '2026-12-09T15:50:01.000Z',
   'dateOfPublishing': '2019-12-09T15:50:01.000Z',
   'archiveStatusMessage': 'Stored on primary disk and on tape',
   'retrieveStatusMessage': 'string',
   'archiveReturnMessage': {'string': 'string'},
   'retrieveReturnMessage': {'string': 'string'},
   'exportedTo': 'string'},
  'principalInvestigator': 'Dorothea Pfeiffer',
  'creationLocation': 'NMX',
  'scientificMetadata': {'id': 3, 'datetime': '2019-04-30T11:49:36.672Z'},
  'endTime': '2016-12-09T15:50:01.000Z',
  'proposalId': '2LG2QT',
  'sampleId': 'samplenmx0075',
  'pid': '20.500.12269/BRIGHTNESS/NMX0075'}]
The results list can be indexed for the file location (for example)
In [39]:
print(results[0]["sourceFolder"])

/users/detector/experiments/beamInstrumentation/FMC-PICO-evaluation
In [ ]:
In [ ]:
In [ ]:
In [127]:
In [ ]:
In [ ]:
In [ ]: